Genetic programming for knowledge discovery in chest-pain diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{bojarczuk:2000:kdcp,

• author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas",
• title = "Genetic programming for knowledge discovery in chest-pain diagnosis",
• journal = "IEEE Engineering in Medicine and Biology Magazine",
• year = "2000",
• volume = "19",
• number = "4",
• pages = "38--44",
• month = jul # "-" # aug,
• keywords = "genetic algorithms, genetic programming, data mining, knowledge discovery, chest-pain diagnosis, predictive accuracy, rule set, comprehensible rules, background knowledge, preprocessing step, data sets, medical applications",
• ISSN = "0739-5175",
• URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf",
• URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html",
• URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-EMB-2000.ps",
• URL = "http://citeseer.ist.psu.edu/459907.html",
• size = "7 pages",
• abstract = "Explores a promising data mining approach. Despite the small number of examples available in the authors' application domain (taking into account the large number of attributes), the results of their experiments can be considered very promising. The discovered rules had good performance concerning predictive accuracy, considering both the rule set as a whole and each individual rule. Furthermore, what is more important from a data mining viewpoint, the system discovered some comprehensible rules. It is interesting to note that the system achieved very consistent results by working from {"}tabula rasa,{"} without any background knowledge, and with a small number of examples. The authors emphasize that their system is still in an experiment in the research stage of development. Therefore, the results presented here should not be used alone for real-world diagnoses without consulting a physician. Future research includes a careful selection of attributes in a preprocessing step, so as to reduce the number of attributes (and the corresponding search space) given to the GP. Attribute selection is a very active research area in data mining. Given the results obtained so far, GP has been demonstrated to be a really useful data mining tool, but future work should also include the application of the GP system proposed here to other data sets, to further validate the results reported in this article.",
• notes = "lilgp",

}

Genetic Programming entries for Celia Cristina Bojarczuk Heitor Silverio Lopes Alex Alves Freitas

Citations